fs.dmred: Feature selection for PCA, FA, and (G)NDA

In nda: Generalized Network-Based Dimensionality Reduction and Analysis

Feature selection for PCA, FA, and (G)NDA

Description

This function drops variables that have low communality values and/or are common indicators (i.e., correlates more than one latent variables).

Usage

fs.dimred(fn,DF,min_comm=0.25,com_comm=0.25)

Arguments

fn	It is a list variable of the output of a principal (PCA), a fa (FA), or an ndr (NDA) function.
DF	Numeric data frame, or a numeric matrix of the data table
min_comm	Scalar between 0 to 1. Minimal communality value, which a variable has to be achieved. The default value is 0.25.
com_comm	Scalar between 0 to 1. The minimal difference value between loadings. The default value is 0.25.

Details

This function only works with principal, and fa, and ndr functions.

This function drops each variable that has a low communality value (under min_comm value). In other words, that variable does not fit enough of any latent variable.

This function also drops so-called common indicators, which correlate highly with more than one latent variable. And the difference in the correlation is either lower than the com_comm value or the greatest absolute factor loading value is not twice greater than the second greatest factor loading.

Value

dropped_low	Numeric data frame or numeric matrix. Set of indicators (i.e. variables), which are dropped by their low communalities. This value is NULL if a correlation matrix is used as an input or there is no dropped indicator.
dropped_com	Numeric data frame or numeric matrix. Set of dropped common indicators (i.e. common variables). This value is NULL if a correlation matrix is used as an input or there is no dropped indicator.
remain_DF	Numeric data frame or numeric matrix. Set of retained indicators
...	Other outputs came from

Author(s)

Zsolt T. Kosztyan*, Marcell T. Kurbucz, Attila I. Katona

e-mail*: [e-mail]

References

Abonyi, J., Czvetkó, T., Kosztyán, Z. T., & Héberger, K. (2022). Factor analysis, sparse PCA, and Sum of Ranking Differences-based improvements of the Promethee-GAIA multicriteria decision support technique. Plos one, 17(2), e0264277. doi:10.1371/journal.pone.0264277

See Also

psych::principal, psych::fa, ndr.

Examples

data<-I40_2020

library(psych)

# Principal Component Analysis (PCA)

pca<-principal(data,nfactors=2,covar=TRUE)
pca

# Feature selection with default values

PCA<-fs.dimred(pca,data)
PCA

# List of dropped, low communality value indicators
print(colnames(PCA$dropped_low))

# List of dropped, common communality value indicators
print(colnames(PCA$dropped_com))

# List of retained indicators
print(colnames(PCA$retained_DF))


# Principal Component Analysis (PCA) of correlation matrix

pca<-principal(cor(data,method="spearman"),nfactors=2,covar=TRUE)
pca

# Feature selection
min_comm<-0.25 # Minimal communality value
com_comm<-0.20 # Minimal common communality value

PCA<-fs.dimred(pca,cor(data,method="spearman"),min_comm,com_comm)
PCA

nda documentation built on Oct. 14, 2024, 5:10 p.m.